基于直觉模糊AWD方法的供应商选择

供应商选择是供应链管理的关键环节之一.运用直觉模糊精确加权(AWD)和复杂比例评价(COPRAS)方法对供应商选择模型进行了研究,构建了基于直觉模糊精确加权的供应商选择的COPRAS模型.模型将供应商评价值的语言变量转化为直觉模糊数,运用直觉模糊的精确加权方法分别确定各个评价指标的精确权重和同一指标下各个供应商的精确权重,极大地提高了指标权重确定的科学性,然后结合COPRAS方法选择供应商.最后将构建的供应商选择模型在实例中进行了应用,并结合各供应商效用度得出了各个供应商的优先顺序,获得了最佳供应商选择方案,验证了所提出方法的可行性和有效性.     

H~∞上加权复合算子与微分算子的乘积(英文)

本文研究了加权复合算子与微分算了的乘积.利用复函数理论,得到了H~∞上加权复合算子与微分算了乘积的有界性及紧性刻画.     

舰艇对抗微分对策建模研究

编队舰艇对抗攻击瞬时运动状态过程的完整描述是一个尚未很好解决的技术难点，本就驱逐舰对多艘导弹快挺对抗攻击建模问题进行了研究，建立了攻击时间尽可能短的定量微分对策模型，并给出了具体实现方法，中模型和方法可为实时舰艇作战指挥系统的研制、开发提供技术支持及理论依据。     

一类具有离散谱的对称微分算子

研究了一类2n-阶线性微分算子在加权Hilbert空间中的谱.通过对加权Sobolev空间H     

加权非等间隔灰色Verhulst模型及其应用

针对建筑物沉降监测数据量少、信息贫乏,且具非等时距、呈"S"形变化的特点,建立了能够反映饱和状态的非等间隔灰色Verhulst模型.因早期建模数据对模型预测精度贡献较小,而靠近预测时间的建模数据贡献则较大,故建模时按时间先后对建模数据赋予了不同的权重,以此体现贡献大小.实证分析表明,采用数据加权能够提高模型的预测精度,从对建模以外的监测数据预测结果来看,效果优于已有一些模型.     

加权几何平均变权组合预测方法及其应用

加权几何平均组合预测模型是一种常用的非线性组合预测方法.为了提高加权几何平均组合预测模型的预测和拟合精度,给出了一类加权几何平均变权重组合预测方法.最后将该变权组合预测模型应用于我国天然气产量的预测.计算结果表明加权几何平均变权重组合预测模型在时间序列数据的预测中具有一定的优势.     

动态经济学数学建模及稳定化控制分析

通过考虑需求函数和供给函数受到不连续因素的影响以及引进切换型的控制策略,建立由右端不连续微分方程刻画的非线性价格调整模型.利用微分包含理论和Lyapunov稳定性方法分析不连续价格调整模型的有限时间稳定化控制问题,并给出数值模拟实例进行验证说明.最后,结合动态经济学数学建模提出数学建模教学改革的几点建议.     

基于灰色加权马尔可夫链的移动通信市场预测

在传统的用灰色预测模型预测的方法基础上,建立了灰色加权马尔可夫链模型.以中国移动通信市场预测作为实例,介绍了使用这种模型的方法与步骤.灰色加权马尔可夫链模型既考虑了从时间序列中挖掘数据的演变规律,又通过规范化各阶自相关系数为权重,用加权的马尔可夫转移概率矩阵的变换,考虑数据的随机波动,具有严密的科学性,能较好地应用于中国移动通信市场的预测.     

缺失数据下线性模型的半参数估计

通过比较参数方法和非参数方法对选择概率建模的优缺点,基于充分降维的思想提出了一种利用单指标模型对选择概率建模的半参数方法.基于逆概率加权方法和半参数方法,研究了缺失数据下线性模型的统计推断问题.建立的逆概率加权估计方程可以处理不同的数据缺失情形,给出了线性模型中兴趣参数的估计,并证明了它的渐近正态性.最后通过模拟研究说明提出的方法具有较好的有限样本性质.     

高阶加权微分算子谱的离散性

本文研究了加权的高阶微分算子的谱. 利用分解的方法和不等式的估计, 得到了一些高阶对称微分算子的任何自共轭扩张的谱离散的充分条件, 推广了 Pfeiffer , Sun, Glazman 等人的结果,利用这些结果可以判别某些微分算子谱的离散性.     

加权马尔可夫链在农作物年景预测中的应用

首先基于农作物平均年单产量的不确定性、不精确性的特点,利用资料序列的均方差建立农作物丰欠年景的分级标准;然后针对农作物平均年单产量为相依随机变量的特点,采取以规范化的各阶自相关系数为权重,用加权的马尔可夫链模型来预测和分析未来的丰欠年景;最后以西安郊区1980~2002共23年的大白菜平均年单产量资料为实例对该方法进行了具体应用,获得了较为满意的结果,为农作物年景的预测分析提供了一条新的途径.     

Investigation and reduction of variance of a weighted estimate in numerical statistical simulation

The efficiency of the “value” and “partial value” modeling related to the construction of a modeling distribution for an auxiliary random variable by multiplying the initial density by a value function is investigated. The value function usually corresponds to a solution of the adjoint equation. Conditions under which the value modeling of the initial distribution reduces the variance compared to the direct simulation are obtained. It is proved that the variance of the weighted estimate is bounded in the case of the partial value modeling. This proposition provides a basis for a method for determining whether or not the variance of the weighted estimate is bounded. This method uses the majorizing adjoint equation. Using a practically important problem in transport theory as an example, the asymptotic optimization of the distribution of the mean free path is presented. The application of the proposed method of the investigation of the variance boundedness for the analysis of the classical exponential transformation method of simulating the mean free path of a particle in the one-dimensional and the spherical variants is discussed.     

Weighted pseudoinverses and weighted normal pseudosolutions with singular weights

Weighted pseudoinverses with singular weights can be defined by a system of matrix equations. For one of such definitions, necessary and sufficient conditions are given for the corresponding system to have a unique solution. Representations of the pseudoinverses in terms of the characteristic polynomials of symmetrizable and symmetric matrices, as well as their expansions in matrix power series or power products, are obtained. A relationship is found between the weighted pseudoinverses and the weighted normal pseudosolutions, and iterative methods for calculating both pseudoinverses and pseudosolutions are constructed. The properties of the weighted pseudoinverses with singular weights are shown to extend the corresponding properties of weighted pseudoinverses with positive definite weights.     

Preference modeling experiments with surrogate weighting procedures for the PROMETHEE method

One of the main tasks in a multi-criteria decision-making process is to define weights for the evaluation criteria. However, in many situations, the decision-maker (DM) may not be confident about defining specific values for these weights and may prefer to use partial information to represent the values of such weights with surrogate weights. Although for the additive model, the use of surrogate weighting procedures has been already explored in the literature, there is a gap with regard to experimenting with such kind of preference modeling in outranking based methods, such as PROMETHEE, for which there already are applications with surrogate weights in the literature. Thus, this paper presents an experimental study on preference modeling based on simulation so as to increase understanding and acceptance of a recommendation obtained when using surrogate weights within the PROMETHEE method. The main approaches to surrogate weights in the literature (EW, RS, RR and ROC) have been evaluated for choice and ranking problematics throughout statistical procedures, including Kendall's tau coefficient. The surrogate weighting procedure that most faithfully represents a DM's value system according to this analysis is the ROC procedure.     

基于集团序的河南省地市级政府网站绩效评估结果的分析

中国政府网站绩效评估已经走过了10年的发展历程,总结以往的评估结果是为了更好地评估,更好地做好政府网站的建设.提取了第十届河南省地方政府网站评估的结果,对评估方法以及评估结果的有效性进行了分析,提出基于属性的不完全补偿性,应该采取加权积方法而非加权和方法,并运用该方法得到了新的评估结果.为了弥补两种方法带来的差异性,提出借助集团序方法进行结果排序,并通过确定不同的级内级差得到了满意的集团排序结果.     

微分算子方法在积分中的应用注记

指出了微分算子方法的缺陷,结果表明:它不能真正解决不定积分的快捷计算问题,有时甚至不如经典算法方便.     

自动选取多非线性参数初始值的循环搜索法

1几何中心化加权最小二乘法给定观测数据集(x_i,y_i),i=1,2,…,n．(1．1)欲拟合加权非线性模型y_i=f(x_i,θ) ε_i,(1．2)其中模型函数f的参数为θ=(θ_1,…,θ_p)~T,随机观测误差项满足ε_i～N[0,(?)~2m(z_i,λ)],(1．3) m(z_i,λ)为已知正值函数,z_i可以是x_i,也可以是μ_i=f(x_i,θ)或二者的组合,λ为权参数,而(?)~2为未知的方差参数,λ和θ可统称为模型参数．模型的似然函数为     

一类一阶常微分方程初值问题的无穷多解性

应用分离变量法,得到了一类一阶微分方程初值问题u′(t)=b(t)f(u(t)),t0,u(0)=0存在无穷多个解的充分必要条件.并给出了全部解.     

Convergence of Weighted Empirical Measures

This article considers a system with infinitely many interacting particles, starting with a system of n interacting particles that is described by a system of n stochastic differential equations for the time-varying locations and weights. Any particle in the system interacts with others through the weighted empirical measure Uⁿ formed by the sum of weighted Dirac measures on the n particles. Weak convergence of the weighted empirical measure is studied under suitable conditions, such as bounded initial values, and linear growth of drift and diffusion coefficients. Thereafter, the limit of the weighted empirical measures is identified to be a martingale solution of the infinite interacting system.